2017/11/13 by Younes Fadakar Alghalandis, Alghalandis, Younes Fadakar, Davide Elmo +3 · 1 citation
Engineering · #Data Analysis #FOS: Mathematics #FOS: Physical sciences #Geophysics (physics.geo-ph) #Geotechnical Engineering and Analysis #Geotechnical Engineering and Underground Structures #Numerical Analysis (math.NA) #Pattern Formation and Solitons (nlin.PS) #Rock Mechanics and Modeling #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1711.05257
openalex publication_date 2017/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Applications of Discrete Fracture Network (DFN) modeling are becoming increasingly prevalent in engineering analyses involving fractured rock masses. For example, kinematic evaluations of slope or underground excavation stability and the modeling of fluid flow in fractured rock have been shown to benefit significantly from the explicit representation of DFN realizations in the simulations. In practice, due to high computing costs, namely time, a balance must be struck that limits analyses to the consideration of only a few realizations as input. As a stochastic representation, a single realization is only one possibility. It is therefore critical that the selected realizations (possibilities) are able to summarize the range of variations present in the input parameters adequately for the purpose of study or practice. That is, the significance of diversity (dissimilarity) in the generated fracture networks is of great importance and should be assessed prior to further often time-consuming processing stages. We demonstrate here a novel development in the analysis of the similarity between three-dimensional fracture networks, which provides an accurate, efficient and practical solution with comprehensive coverage of model variations. Several examples are presented together with a comparison between the proposed three-dimensional method and existing methods limited to two-dimensional assumptions. It is shown that the two-dimensional similarity methods despite their popularity are heavily biased and poorly represent the reality.